1# Copyright 2014 The Android Open Source Project
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7#      http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14
15import its.image
16import its.device
17import its.caps
18import its.objects
19import os.path
20from matplotlib import pylab
21import matplotlib
22import matplotlib.pyplot
23import numpy
24
25#AE must converge within this number of auto requests for EV
26THREASH_CONVERGE_FOR_EV = 8
27
28def main():
29    """Tests that EV compensation is applied.
30    """
31    LOCKED = 3
32
33    NAME = os.path.basename(__file__).split(".")[0]
34
35    MAX_LUMA_DELTA_THRESH = 0.05
36
37    with its.device.ItsSession() as cam:
38        props = cam.get_camera_properties()
39        its.caps.skip_unless(its.caps.manual_sensor(props) and
40                             its.caps.manual_post_proc(props) and
41                             its.caps.per_frame_control(props) and
42                             its.caps.ev_compensation(props))
43
44        debug = its.caps.debug_mode()
45        largest_yuv = its.objects.get_largest_yuv_format(props)
46        if debug:
47            fmt = largest_yuv
48        else:
49            match_ar = (largest_yuv['width'], largest_yuv['height'])
50            fmt = its.objects.get_smallest_yuv_format(props, match_ar=match_ar)
51
52        ev_compensation_range = props['android.control.aeCompensationRange']
53        range_min = ev_compensation_range[0]
54        range_max = ev_compensation_range[1]
55        ev_per_step = its.objects.rational_to_float(
56                props['android.control.aeCompensationStep'])
57        steps_per_ev = int(round(1.0 / ev_per_step))
58        ev_steps = range(range_min, range_max + 1, steps_per_ev)
59        imid = len(ev_steps) / 2
60        ev_shifts = [pow(2, step * ev_per_step) for step in ev_steps]
61        lumas = []
62
63        # Converge 3A, and lock AE once converged. skip AF trigger as
64        # dark/bright scene could make AF convergence fail and this test
65        # doesn't care the image sharpness.
66        cam.do_3a(ev_comp=0, lock_ae=True, do_af=False)
67
68        for ev in ev_steps:
69
70            # Capture a single shot with the same EV comp and locked AE.
71            req = its.objects.auto_capture_request()
72            req['android.control.aeExposureCompensation'] = ev
73            req["android.control.aeLock"] = True
74            # Use linear tone curve to avoid brightness being impacted
75            # by tone curves.
76            req["android.tonemap.mode"] = 0
77            req["android.tonemap.curveRed"] = [0.0,0.0, 1.0,1.0]
78            req["android.tonemap.curveGreen"] = [0.0,0.0, 1.0,1.0]
79            req["android.tonemap.curveBlue"] = [0.0,0.0, 1.0,1.0]
80            caps = cam.do_capture([req]*THREASH_CONVERGE_FOR_EV, fmt)
81
82            for cap in caps:
83                if (cap['metadata']['android.control.aeState'] == LOCKED):
84                    y = its.image.convert_capture_to_planes(cap)[0]
85                    tile = its.image.get_image_patch(y, 0.45,0.45,0.1,0.1)
86                    lumas.append(its.image.compute_image_means(tile)[0])
87                    break
88            assert(cap['metadata']['android.control.aeState'] == LOCKED)
89
90        print "ev_step_size_in_stops", ev_per_step
91        shift_mid = ev_shifts[imid]
92        luma_normal = lumas[imid] / shift_mid
93        expected_lumas = [min(1.0, luma_normal * ev_shift) for ev_shift in ev_shifts]
94
95        pylab.plot(ev_steps, lumas, 'r')
96        pylab.plot(ev_steps, expected_lumas, 'b')
97        matplotlib.pyplot.savefig("%s_plot_means.png" % (NAME))
98
99        luma_diffs = [expected_lumas[i] - lumas[i] for i in range(len(ev_steps))]
100        max_diff = max(abs(i) for i in luma_diffs)
101        avg_diff = abs(numpy.array(luma_diffs)).mean()
102        print "Max delta between modeled and measured lumas:", max_diff
103        print "Avg delta between modeled and measured lumas:", avg_diff
104        assert(max_diff < MAX_LUMA_DELTA_THRESH)
105
106if __name__ == '__main__':
107    main()
108